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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 2
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 3
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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Reliable AAISM Practice Materials & Real AAISM Exam Answers

ISACA AAISM certification exams play a significant role to verify skills, experience, and knowledge in a specific technology. Enrollment in the ISACA Advanced in AI Security Management (AAISM) Exam AAISM is open to everyone. Participants in the ISACA Advanced in AI Security Management (AAISM) Exam AAISM come from all over the world and receive the credentials for the ISACA AAISM. They can quickly advance their careers in the fiercely competitive market and benefit from certification after earning the ISACA Advanced in AI Security Management (AAISM) Exam AAISM badge.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q120-Q125):

NEW QUESTION # 120
Which of the following would BEST help an organization align its AI initiatives with business objectives?

Answer: B

Explanation:
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
References: AI Security Management™ (AAISM) Body of Knowledge: AI Governance Operating Model- Structures, Roles, and Decision Rights; AAISM Study Guide: Strategic Alignment, Portfolio Oversight, and Accountability Mechanisms.


NEW QUESTION # 121
An organization decides to contract a vendor to implement a new set of AI libraries. Which of the following is MOST important to address in the master service agreement to protect data used during the AI training process?

Answer: A

Explanation:
AAISM emphasizes that the right to audit is the most critical contractual safeguard when outsourcing AI services. This allows the contracting organization to independently verify that the vendor is applying appropriate protections to training data, meeting compliance obligations, and upholding privacy requirements.
Pseudonymization is a technical method, monitoring is operational, and certifications provide external assurance, but none give the direct, enforceable oversight that audit rights provide. In vendor contracts, the right to audit is the primary safeguard for data protection and governance.
References:
AAISM Study Guide - AI Governance and Program Management (Third-Party Contracts and Audit Rights) ISACA AI Security Management - Vendor Governance Controls


NEW QUESTION # 122
A CISO must provide KPIs for the organization's newly deployed AI chatbot. Which metrics are BEST?

Answer: C

Explanation:
AAISM recommends that AI KPIs should emphasize:
* Error rates - measure correctness and reliability
* Bias detection metrics - assess fairness and harm risk
These are the core governance KPIs for AI systems interacting with end users.
Response time (A) is a performance metric but not an AI governance KPI. Customer retention (C) is business- focused. F1 score (D) is useful but not as critical as bias monitoring.
References: AAISM Study Guide - AI Performance, Fairness & Governance Metrics.


NEW QUESTION # 123
Which of the following is the BEST control for preventing deepfakes?

Answer: D

Explanation:
Output provenance verification (e.g., robust watermarking, cryptographic signing, content credentials, and chain-of-custody attestations) is the primary preventive and detective control to combat deepfakes at scale. It enables receivers and downstream systems to verify that media originates from trusted sources and has not been tampered with. While risk assessments (Option B) and governance policies (Option C) set expectations, they do not technically prevent forged media. Input validation (Option D) does not address media authenticity once generated or received.
References:
AAISM Body of Knowledge: Content Authenticity, Watermarking, and Provenance; Trustworthy AI Outputs and Media Integrity Controls.
AAISM Study Guide: Mitigations for Synthetic Media Risks; Watermark/Signature Verification Pipelines; Content Credentials in Enterprise Controls.


NEW QUESTION # 124
An organization is deploying a large language model (LLM) and is concerned that input manipulations may compromise its integrity. Which of the following is the MOST effective way to determine an acceptable risk threshold?

Answer: B

Explanation:
AAISM requires that risk thresholds/tolerances be set by aligning threat likelihood and impact with the organization's business context and risk appetite. Determining "acceptable" risk starts with assessing business impact of credible threats (e.g., prompt injection leading to data exfiltration, policy evasion, or harmful actions), then translating this into control intensity and thresholds. Hard input restrictions (A) and static output caps (C) are blunt measures that may degrade utility without ensuring alignment to risk appetite.
Monitoring (B) is essential for detection, but it does not, by itself, define what level of risk is acceptable.
References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite and Tolerance for AI; Threat Modeling for LLMs; Business Impact Analysis and Risk Acceptance Criteria.


NEW QUESTION # 125
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